multisom.stochastic: Multisom for stochastic version

In multisom: Clustering a Data Set using Multi-SOM Algorithm

Description

This function implements the stochastic version of MultiSOM algorithm.

Usage

multisom.stochastic(data = NULL, xheight = 7, xwidth = 7,
                  topo = c("rectangular", "hexagonal"),
                  neighbouhood.fct =c("bubble","gaussian"),
                  dist.fcts = NULL, rlen = 100,alpha = c(0.05, 0.01),
                  radius = c(2, 1.5, 1.2, 1), index = "all")

Arguments

data	the data matrix of observations
xheight	the x-dimension of the map
xwidth	the y-dimension of the map
topo	the topology used to build the grid.The following are permitted: "hexagonal" "rectangular"
neighbouhood.fct	the neighbouhood function type. The following are permitted: "gaussian" "bubble"
dist.fcts	The metric used to determine the distance function. Possible choices are: "sumofsquares" "euclidean" "manhattan" "tanimoto"
rlen	the maximum number of iterations to be done
alpha	learning rate, a vector of two numbers indicating the amount of change. Default is to decline linearly from 0.05 to 0.01 over rlen updates.
radius	the radius of the neighbourhood, either given as a single number or a vector (start, stop). If it is given as a single number the radius will run from the given number to the negative value of that number; as soon as the neighbourhood gets smaller than one only the winning unit will be updated.
index	vector of the index to be calculated. This should be one of : "db", "dunn", "silhouette", "ptbiserial", "ch", "cindex", "ratkowsky", "mcclain", "gamma", "gplus", "tau", "ccc", "scott", "marriot", "trcovw", "tracew", "friedman", "rubin", "ball", "sdbw", "dindex", "hubert", "sv", "xie-beni", "hartigan", "ssi", "xu", "rayturi", "pbm", "banfeld", "all" (all indices will be used)

Value

All.index.by.layer	Values of indices for each layer.
Best.nc	Best number of clusters proposed by each index and the corresponding index value.
Best.partition	Partition that corresponds to the best number of clusters

Author(s)

Sarra Chair and Malika Charrad

Examples

## A real data example

data<-as.matrix(iris[,-c(5)])

res<-multisom.stochastic(data, xheight = 8, xwidth = 8,"hexagonal","gaussian",
                    dist.fcts = NULL, rlen = 100,alpha = c(0.05, 0.01),
                    radius = c(2, 1.5, 1.2, 1),c("db","ratkowsky","dunn"))

res$All.index.by.layer
res$Best.nc

multisom documentation built on May 2, 2019, 1:27 p.m.